{"schemaVersion":"jobsearcher.job.v1","id":"3a3e3d28ecb33a8613d8d669","url":"https://jobsearcher.com/jobs/3a3e3d28ecb33a8613d8d669","canonicalUrl":"https://jobsearcher.com/jobs/3a3e3d28ecb33a8613d8d669","title":"Senior MLOps Engineer","description":"About the Position\r\nThe client is focused on improving and scaling machine learning systems. They need a senior MLOps engineer to build end‑to‑end ML pipelines in the cloud, automate model training and deployment, and ensure production ML systems are monitored, reliable, and scalable.Start:December 1, 2025Key ResponsibilitiesAutomate machine learning model training and deployment processes using CI/CD pipelinesBuild end-to-end MLOps pipelines in cloud platforms (AWS / GCP / Azure)Implement monitoring and observability for ML models in production environmentsOptimize infrastructure for ML workloads to improve reliability, scalability, and efficiencyDeploy and manage containerized ML applications using Docker and KubernetesImplement model versioning, experiment tracking, and model registry solutionsSet up data pipelines and feature stores for ML model trainingEnsure ML model performance monitoring, drift detection, and retraining automationCollaborate with data scientists to operationalize ML models from development to productionImplement infrastructure as code for ML infrastructure using Terraform or similar toolsReports to:Client’s Engineering Manager / CTOCollaborates with:Data Science team, Engineering teams, DevOps teamTechnologies\r\nMust-have:MLOps practices, CI/CD for ML (GitHub Actions, GitLab CI, Azure DevOps), Docker, Kubernetes, Cloud platforms (AWS / GCP / Azure), Python, Infrastructure as Code (Terraform), ML frameworks (TensorFlow, PyTorch, scikit-learn), Model deployment (SageMaker, Vertex AI, Azure ML, or Kubeflow)Nice-to-have:MLflow, Weights & Biases, DVC, Feature stores (Feast, Tecton), Model monitoring (Evidently, WhyLabs), Apache Airflow, Spark, Ray, Helm, ArgoCD, Prometheus, Grafana, Data versioning, A/B testing for modelsSoft SkillsFluent English (conversational and written)Strong problem-solving and analytical skillsAbility to work independently and implement ML processes end-to-endCollaboration skills working with data scientists and engineersUnderstanding of ML model lifecycle from data to productionHighly self‑managed and able to plan, estimate, and execute tasksChallenges & Milestones\r\nFirst 90 Days:Assess current ML infrastructure, implement initial MLOps automation, set up model monitoring for production modelsMonths 3-6:Build end-to-end ML pipelines with automated training and deployment, implement experiment tracking and model registry, optimize infrastructure costsMonths 6-12:Full MLOps platform operational with automated retraining, drift detection, A/B testing capabilities, and scalable infrastructure supporting multiple ML modelsWorking Hours\r\nFull-time (40 hours/week), RemoteFlexible hours with reasonable overlap for team collaborationWe are seeking aSenior MLOps Engineerto build and scale machine learning systems in the cloud. This role focuses on automating ML model training, deployment, and monitoring to ensure reliable production ML operations.#J-18808-Ljbffr","company":"Apprecode","rawCompany":"apprecode","city":"Middletown","state":"NJ","isRemote":false,"isActive":false,"createdAt":"2026-10-04T02:28:21.523Z","occupations":[{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"}],"industries":[{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Senior MLOps Engineer","description":"About the Position\r\nThe client is focused on improving and scaling machine learning systems. They need a senior MLOps engineer to build end‑to‑end ML pipelines in the cloud, automate model training and deployment, and ensure production ML systems are monitored, reliable, and scalable.Start:December 1, 2025Key ResponsibilitiesAutomate machine learning model training and deployment processes using CI/CD pipelinesBuild end-to-end MLOps pipelines in cloud platforms (AWS / GCP / Azure)Implement monitoring and observability for ML models in production environmentsOptimize infrastructure for ML workloads to improve reliability, scalability, and efficiencyDeploy and manage containerized ML applications using Docker and KubernetesImplement model versioning, experiment tracking, and model registry solutionsSet up data pipelines and feature stores for ML model trainingEnsure ML model performance monitoring, drift detection, and retraining automationCollaborate with data scientists to operationalize ML models from development to productionImplement infrastructure as code for ML infrastructure using Terraform or similar toolsReports to:Client’s Engineering Manager / CTOCollaborates with:Data Science team, Engineering teams, DevOps teamTechnologies\r\nMust-have:MLOps practices, CI/CD for ML (GitHub Actions, GitLab CI, Azure DevOps), Docker, Kubernetes, Cloud platforms (AWS / GCP / Azure), Python, Infrastructure as Code (Terraform), ML frameworks (TensorFlow, PyTorch, scikit-learn), Model deployment (SageMaker, Vertex AI, Azure ML, or Kubeflow)Nice-to-have:MLflow, Weights & Biases, DVC, Feature stores (Feast, Tecton), Model monitoring (Evidently, WhyLabs), Apache Airflow, Spark, Ray, Helm, ArgoCD, Prometheus, Grafana, Data versioning, A/B testing for modelsSoft SkillsFluent English (conversational and written)Strong problem-solving and analytical skillsAbility to work independently and implement ML processes end-to-endCollaboration skills working with data scientists and engineersUnderstanding of ML model lifecycle from data to productionHighly self‑managed and able to plan, estimate, and execute tasksChallenges & Milestones\r\nFirst 90 Days:Assess current ML infrastructure, implement initial MLOps automation, set up model monitoring for production modelsMonths 3-6:Build end-to-end ML pipelines with automated training and deployment, implement experiment tracking and model registry, optimize infrastructure costsMonths 6-12:Full MLOps platform operational with automated retraining, drift detection, A/B testing capabilities, and scalable infrastructure supporting multiple ML modelsWorking Hours\r\nFull-time (40 hours/week), RemoteFlexible hours with reasonable overlap for team collaborationWe are seeking aSenior MLOps Engineerto build and scale machine learning systems in the cloud. This role focuses on automating ML model training, deployment, and monitoring to ensure reliable production ML operations.#J-18808-Ljbffr","datePosted":"2026-10-04T02:28:21.523Z","dateModified":"2026-10-04T02:28:21.523Z","hiringOrganization":{"@type":"Organization","name":"Apprecode","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Middletown","addressRegion":"NJ","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"3a3e3d28ecb33a8613d8d669"},"url":"https://jobsearcher.com/jobs/3a3e3d28ecb33a8613d8d669"}}